Health Information System Quality and Benefits


Over the recent past, health care sector in both developed and developing countries have exhibited depressed information system structure (Porter &Teisberg, 2004, p. 4).

Health Care structure is different from other sectors of the economy because of high degree of regulation, massive state investment and associated low demand for effectiveness and efficiency in public hospitals and lack of patient geared policies (Mettler et al., 2007, p. 10).

Most of the healthcare funds are wasted on inefficient processes (Porter &Teisberg, 2004, p. 65).

Information and communication technology in the health sector has not only increased effectiveness and efficiency of the healthcare service but also quality of service delivery (World Health Organization 2005, p. 4).

Data Quality in HealthCare

The is no conventional agreement on the definition of data quality (Kahn et al., 2002, p.12).

Researchers use the following approaches to define data quality criteria: Empirical, theoretical, literature and practitioner-based criteria(Price &Shanks, 2005, p.88).

Empirical study approach relies mainly on the consumer feedback to develop quality criteria and grade them into classes (Price &Shanks, 2005, p.88).

Practitioner based approaches are focused on impromptu observations and experiences of the medical practitioners thus lacks rigor (Price &Shanks, 2005, p.90).

Health Information Systems

Health Information system refers to the data storage and processing systems that assist in healthcare service delivery (Kitch & Yasnoff, 2002, p. 114).

The most common and widely used systems include the billing and eligibility check system for insurance and state programs. However, these systems are not medical support system since they do not track and assess healthcare (Kitch & Yasnoff, 2002, p. 114).

Health Information systems support, keep track and analyze service delivery in the healthcare organizations (Kitch & Yasnoff, 2002, p. 114).

The basic fundamental healthcare system is the Electronic Medical Record (EMR). EMR includes patient’s history, examinations, test results, prescriptions, and comments of the physicians among other clinical details (Ammenwerth et al., 2003, p. 126).

Electronic Health Records (EHR) was a term used to refer to all the patients’ medical information across various healthcare organizations, but is now used interchangeably with Electronic Medical Records (EMR) (Ammenwerth et al., 2003, p. 126).

The major difference between EMR and other conventional paper records is that the records can easily be shared and analyzed(Ammenwerth et al., 2003, p. 127).

Computerized Physician Order Entry systems enables physicians to organize procedures electronically (Kitch & Yasnoff, 2002, p. 33).

Clinical pharmacies Support Systems are based on medical principles and scientific studies and help physicians to suggest diagnoses and treatments to patients (Porter &Teisberg, 2004, p.67).

Public health organizations and institutions use information communication technology to facilitate management of data (Ammenwerth et al., 2003, p. 125).

Public health information systems have been developed to support specific healthcare programs such as: immunization, surveillance of diseases, school health, among others (Mettler et al., 2007, p. 11).

These systems have varying data and are integrated among government healthcare organizations, thus provide vital information for public health decision support and medical research (Ammenwerth et al., 2003, p. 125).

Reasons for Slow Adoption of Health Information Systems

Natural resistance of people to change to new ideas (Porter &Teisberg, 2004, p.67).

Cost benefit analysis (Mettler et al., 2007, p. 22).

Disparity in the quality of healthcare service delivery among different organizations (Ammenwerth et al., 2003, p. 126).

Benefits of Health Information System

Sharing of information among different healthcare providers and organizations (World Health Organization 2005, p. 10).

Health information system can automatically assess the possible adverse drug interactions, recommended prescription guidelines and allergic reactions to specific drugs (World Health Organization 2005, p. 10).

Computerized Physician Order Entry systems monitors adherence to medical rules and regulations based on modern research results and the available hospital results to come up with the most effective and efficient procedures (Kitch & Yasnoff, 2002, p. 33).

Health information system has helped in minimizing effects of human errors in the medical field (Ammenwerth et al., 2003, p. 127).

The government uses information from the wider health information system network to tackle the challenges facing the healthcare sector and the healthcare practitioners (Kitch & Yasnoff, 2002, p. 33).


Health information systems has increased effectiveness and efficiency of healthcare services and also the quality of service delivery (Porter &Teisberg, 2004, p. 64).

Implementation and adoption of health information systems has been quite slow to due a number factors including the cost/benefit analysis, conservative nature of most clinicians among others (Mettler et al., 2007, p. 22).

Public health organizations and institutions have used information communication technology to facilitate data and information management (Ammenwerth et al., 2003, p. 125).

Health information systems in the public sector still lack integration and interoperability (Ammenwerth et al., 2003, p. 126-127).


Ammenwerth, E., Graber, S., Herrmann, G., Burkle, T., & Konig, J. (2003). Evaluation of Health information systems: problems and challenges. International Journal of Medical Informatics, 71, 125-135.

Kahn, B., Strong, D., & Wang, R.Y. (2002). Information quality benchmarks: Product and service performance. Communications of the ACM, 45 (4), 184-192.

Kitch, P., & Yasnoff, W. A. (2002). Assessing the Value of Information Systems. New York: Springer-Verlag.

Mettler, T., Rohner, P., & Winter, R. (2007). Factors influencing networkability in the health care sector – Derivation and empirical validation. Proceedings of the 12th International Symposium on Health Information Management Research, ISHIMR 2007, Sheffield, UK.

Porter, M., & Olmsted Teisberg, E. (2004). Redefining competition in health care. Harvard Business Review, 82 (6), 64-76.

Price, R., & Shanks, G. (2005). A semiotic information quality framework: Development and comparative analysis. Journal of Information Technology, 20 (2), 88-102.

Wand, Y., & Wang, Y.R. (1996). Anchoring data quality dimensions in ontological foundations. Communications of the ACM, 39 (11), 86-95.

World Health Organization (2005). E-Health. Web.

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